基于和声搜索算法优化支持向量机的突发暴恐事件分级研究

Optimizing Parameters of Support Vector Machine Using Harmony Search Algorithm for Emergency Classification of Terrorist Attacks

突发暴恐事件分级具有重要作用,能够保证预案合理执行和应急资源优化配置。提出基于和声搜索算法优化的支持向量机的分级模型,用于突发暴恐事件分级研究。和声搜索算法优化支持向量机参数,支持向量机提供学习和曲线拟合,同时根据准确度、精确度和敏感度指标评估混战智能分类模型的绩效。利用全球反恐数据库中2008年至2013年我国暴恐事件数据进行测试,并与支持向量机、分类与回归决策树(CART)和C5.0方法进行对比,结果表明分级方法可行且有效,能够为突发暴恐事件管理提供预警和决策支持信息。

The classification on terrorist attacks plays an important role to ensure optimal allocation of emergency resources and reasona- ble implementation for emergency plan. This study proposes a model to integrate a harmony search with a support vector machine （SVM） to research classification of terrorist attacks. The support vector machine provides learning and curve fitting while harmony search optimi- zes support vector machine parameters. Measures in term of accuracy, precision and sensitivity are used for performance evaluation of proposed hybrid intelligence classification model. The data of global terrorism database from 2008 to 2013 in china is used for testing, and experimental comparisons indicate that HS-based SVM achieves better accuracy compared to SVM, CART and C5.0. Experimental results show that HSSVM is a feasible approach dealing with emergency classification problems, and provides warning and decision support information needed to manage emergency terrorist attacks.